19,821 research outputs found

    A Model of Layered Architectures

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    Architectural styles and patterns play an important role in software engineering. One of the most known ones is the layered architecture style. However, this style is usually only stated informally, which may cause problems such as ambiguity, wrong conclusions, and difficulty when checking the conformance of a system to the style. We address these problems by providing a formal, denotational semantics of the layered architecture style. Mainly, we present a sufficiently abstract and rigorous description of layered architectures. Loosely speaking, a layered architecture consists of a hierarchy of layers, in which services communicate via ports. A layer is modeled as a relation between used and provided services, and layer composition is defined by means of relational composition. Furthermore, we provide a formal definition for the notions of syntactic and semantic dependency between the layers. We show that these dependencies are not comparable in general. Moreover, we identify sufficient conditions under which, in an intuitive sense which we make precise in our treatment, the semantic dependency implies, is implied by, or even coincides with the reflexive-transitive closure of the syntactic dependency. Our results provide a technology-independent characterization of the layered architecture style, which may be used by software architects to ensure that a system is indeed built according to that style.Comment: In Proceedings FESCA 2015, arXiv:1503.0437

    Interference and communications among active network applications

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    This paper focuses on active networks applications and in particular on the possible interactions among these applications. Active networking is a very promising research field which has been developed recently, and which poses several interesting challenges to network designers. A number of proposals for e±cient active network architectures are already to be found in the literature. However, how two or more active network applications may interact has not being investigated so far. In this work, we consider a number of applications that have been designed to exploit the main features of active networks and we discuss what are the main benefits that these applications may derive from them. Then, we introduce some forms of interaction including interference and communications among applications, and identify the components of an active network architecture that are needed to support these forms of interaction. We conclude by presenting a brief example of an active network application exploiting the concept of interaction

    Markovian Characterisation of H.264/SVC scalable video

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    In this paper, a multivariate Markovian traffic: model is proposed to characterise H.264/SVC scalable video traces. Parametrisation by a genetic algorithm results in models with a limited state space which accurately capture. both the temporal and the inter-layer correlation of the traces. A simulation study further shows that the model is capable of predicting performance of video streaming in various networking scenarios

    Evolving Ensemble Fuzzy Classifier

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    The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
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